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Investigating Memory Effect and Reaction Heterogeneity in Porous Electrodes for Lithium-Ion Batteries Using Synchrotron X-ray Techniques and Computational Modeling
No full textModern lithium-ion batteries (LIBs) widely employ porous electrodes, which are critical for commercial applications. Optimizing the structural design of these electrodes at both the particle and electrode scales has proven effective in enhancing reaction kinetics and reducing manufacturing costs. Thick porous electrodes, in particular, offer a promising strategy to improve energy density by increasing the weight and volume fractions of active materials in battery cells. However, their adoption is hindered by limitations such as reduced rate capability and cycling performance, posing challenges for their practical deployment.
The inferior performance of thick electrodes is often attributed to reaction heterogeneity arising from sluggish charge transport kinetics. In the first part of this study, however, we reveal a previously underappreciated factor: the role of electrode material thermodynamics in inducing reaction heterogeneity. Using a combination of two-dimensional X-ray fluorescence microscopy (XRF) and X-ray absorption near-edge structure (XANES) spectroscopy, we mapped the state of charge (SOC) across thick electrode cross-sections with nano- and micro-scale resolutions. This correlative technique was applied to investigate thick porous electrodes composed of two common active materials: LiNi₀.₆Mn₀.₂Co₀.₂O₂ (NMC622) and LiFePO₄ (LFP). 2D XRF-XANES results, corroborated by numerical simulations, demonstrate that reaction heterogeneity along the electrode depth is strongly influenced by the thermodynamic properties of the active materials. These findings provide key
insights for material selection and cycling protocol optimization to improve the capacity and performance of thick electrodes.
Beyond poor rate capability and cycling performance associated with reaction heterogeneity, the second part of this thesis reports yet another challenge faced by thick electrodes, i.e. the "memory effect," which is also intrinsically linked to reaction nonuniformity. This phenomenon manifests itself when thick electro
Measuring Epileptogenicity through Multilayer Graph Structures
No full textThe success of resection or neurostimulation treatments in patients with drug-resistant epilepsy heavily relies on the accurate identification of the epileptogenic zone (EZ), the specific brain regions necessary for seizure generation. While existing automated methods have shown promising performance, many rely on unidimensional characterizations of the epileptogenic network, compressing its hidden complexity and potentially overlooking the clinical intricacies of epilepsy. To address this gap, we introduce EZ-Meter, a multilayer network-based framework that integrates diverse and interpretable features to provide a more holistic understanding of the condition. EZ-Meter constructs three distinct graphical layers capturing nonlinear spectral and temporal relationships during pre-ictal, ictal, and interictal intervals, each reflecting a distinct epileptic state. Features extracted from this multilayer graph are then used in a machine learning algorithm to identify the EZ, followed by a post-processing step that incorporates the spatial properties of electrodes. We evaluate EZ-Meter on stereo-electroencephalography (SEEG) recordings from patients with drug-refractory epilepsy who underwent successful surgical interventions. We achieve an average AUC of 94.3% on the holdout dataset, outperforming the baseline approaches. Additionally, the analysis of the extracted features enables neurophysiologically plausible interpretations of the underlying phenomena. Our findings highlight the potential of multilayer network-based modeling for improving both the performance and interpretability of EZ identification
Toward a wirelessly-powered, fascicle-specific system for peripheral nerve stimulation
No full textRestoring fine movement control is essential for those with paresis and plegia, and peripheral nerve stimulation offers a powerful means to reestablish coordinated movement by activating muscles through intact peripheral pathways. Current clinically available nerve stimulators, however, deliver current at the nerve trunk, often activating multiple fascicles simultaneously and limiting fine motor control. To address this, research has advanced toward fascicle-specific stimulation, selectively recruiting individual fascicles within a mixed nerve to restore more natural, coordinated movements. Still, these approaches remain constrained in longevity: intraneural electrodes risk excessive fibrotic encapsulation that can render them ineffective while multi-contact, extraneural cuffs avoid tissue damage but depend on bulky implantable pulse generators and long leads prone to failure. This thesis presents a novel wirelessly-powered, multi-polar helical nerve cuff electrode developed for long-term, fascicle-specific stimulation. This design minimizes form factor, enabling distributed nerve stimulation with real-time programmability to adjust stimulation parameters and target specific muscles. With such technology, we will enable peripheral nerve stimulation with fine resolution to facilitate successful movement restoration
Building the Data and Evaluation Capacity of Nonprofits in the Greater Houston Area: Examining the Role of United Way of Greater Houston’s Coffee & Quality Initiative
Get PDFIn order to help broaden the skills, abilities, and application of data and evaluation among nonprofit organizations in the Greater Houston region, United Way of Greater Houston launched its Coffee & Quality initiative in fall of 2019. With a commitment to continuously improving its offerings and services, the United Way team in charge of Coffee & Quality, in partnership with the Houston Population Research Center at the Kinder Institute for Urban Research, has administered open-access surveys to nonprofits around the Greater Houston area for the past 2 years. The goals of the surveys were to gather feedback on the Coffee & Quality initiative, and better understand the data and evaluation practices of individuals working in the nonprofit sector and the organizations where they worked. This research brief is a summary of what has been learned from these first two survey
Space–Time Isogeometric Analysis of the Taylor–Couette Flow with Thermal Coupling
No full textThis thesis focuses on accurate representation of the flow patterns in computational analysis of the Taylor-Couette flow with thermal coupling. This requires methods that can, with a reasonable computational cost, represent the circular geometry accurately and provide a high-fidelity thermo-fluid solution. The key methods in the computational analysis are the Space-Time Variational Multiscale (ST-VMS) method for the Navier-Stokes equations of incompressible flows with thermal coupling and the ST Isogeometric Analysis (ST-IGA). The ST framework provides higher-order accuracy in general. The VMS feature of the ST-VMS addresses the computational challenges associated with the multiscale nature of the thermo-fluid behavior. The ST-IGA, with IGA basis functions in space, enables accurate and smooth representation of the circular geometry and increased accuracy in the thermo-fluid solution. The thermal coupling enables investigation of how the temperature-dependent properties of oil influence the thermo-fluid behavior. In addition to accounting for the variation of density with temperature, the investigation accounts for the variation of viscosity and thermal conductivity with temperature. They all play a significant role in thermo-fluid behavior. To better understand viscosity's temperature dependence, two different models are used in representing that dependence, with a comparative evaluation of their influence on the thermo-fluid behavior
AI in the Classroom: Developing a Scale Using Students’ Attitudes
No full textAs educators and students begin to understand the benefits and limitations of using Generative Artificial Intelligence (AI), such as ChatGPT, for learning, there is a greater need for shared understanding and language of how and when AI should be allowed in the classroom. This mixed-methods study examines college students’ attitudes toward using AI for classwork and assignments to develop a standardized AI usage scale and evaluate its perceived utility. Researchers gathered AI policy proposals from undergraduates in three psychology courses, revealing views from restrictive to liberal use. ChatGPT analyzed these statements to create a 9-point AI usage scale, from none to significant use. Correlational analyses of survey data indica te that students who see AI as beneficial fear violating the honor code but believe a standardized scale would make them more likely to use AI. These findings suggest students recognize AI’s potential and that clear guidelines would increase confidence in its use. Future research should evaluate the developed scale’s usability and explore whether employing it in classrooms affects students’ confidence and likelihood of using AI
Quantum Algebraic Geometry Codes
No full textQuantum error correction is an essential aspect of quantum information theory, providing protection for quantum states against noise and decoherence. This thesis investigates the construction of quantum error correction codes derived from classical algebraic geometry (AG) codes. We present two distinct construction techniques, highlighting the flexibility and self-orthogonality of AG codes, and demonstrate their ability to produce asymptotically good quantum codes. Additionally, we explore strategies to fine-tune the parameters of classical AG codes, ensuring they possess the desired properties for quantum code construction. This work serves as a comprehensive guide to the fundamental concepts and common methodologies underlying quantum algebraic geometry codes
Rad5 Replication Fork Rescue Mechanism Elucidation with a Structural Perspective
No full textStalled replication forks are frail structures with exposed single stranded (ss) DNA regions that are prone to digestion breaks. These are highly cytotoxic and energetically expensive to repair. Organisms have systems in place to protect and resolve stalled forks so replication can continue. Failure with said systems is closely associated with cell death and cancer.
Fork reversal is a rescue mechanism for stalled replication forks that as the name implies, relies on re-zipping of the opened parental strand until a four-way double stranded intermediate is obtained. Helicase-like Transcription Factor (HLTF) in humans, and its low eukaryote ortholog Rad5, are prime examples of fork reversal enzymes. Interest in HLTF and its role in oncogenesis has been hampered by the complexity of expressing and manipulating this frail enzyme. Despite the hurdles, understanding the fork reversal mechanism is critical to identify how organisms discern between rescue events and disease progression.
Exploiting the thermally resilient proteome of C. thermophilum, and functional homolog Rad5, allowed the study and elucidation of mechanistic details of enzyme-mediated fork reversal. Validation of Rad5 from a non-model organism exhibited the advantages of thermally stable enzymes and cements the future
use of alternatives to overcome physical and logistical issues that plague recombinant enzyme production. Concerted biochemical and structural studies were performed to gain mechanistic information on Rad5 reversal of the replication fork. Our findings showed a regulatory effect by the enzyme’s distal n-terminal domain, that is linked to substrate specificity. Our work also identified the strand Rad5 translocating of DNA occurs on, which further solidifies our hypothesized mechanism and counters the enzyme’s historical description as an annealing helicase. Finally sample optimization and complex formation studies lay the groundwork for continuing structural tests
Graph-based Learning for Efficient Resource Allocation and Management in Wireless Networks
Get PDFWireless network optimization faces challenges in several aspects. System-level objective functions are typically non-convex due to multiuser interference, rendering NP-hard problems. Moreover, real-world factors like delay and energy requirements could translate into non-convex constraints. As a result, traditional practices tend to be suboptimal and often rely on computationally expensive iterations or simulations. This inefficiency limits the design and management of scalable wireless networks.
To improve efficiency, this thesis leverages deep learning techniques, with a focus on the permutation-equivariant graph neural networks (GNNs), across various wireless applications. The first part of this thesis presents a graph-based trainable framework for power allocation, namely the unfolded successive concave approximation (USCA), to maximize weighted-sum energy efficiency (WSEE) in wireless interference networks. The second part revisits power allocation and, by devising a primal-dual (PD) learning framework, handles non-convex constraints specific to the context of wireless federated learning (FL). Finally, the third part creates a learnable digital twin (DT) for efficient network evaluation, a thousandfold more efficient tool than simulators for network design and management. These three bodies of work demonstrate comprehensive theoretical and experimental results highlighting the superior performance of the proposed architectures over existing approaches.
Overall, this work seeks to integrate deep learning into wireless applications by innovating specialized architectures that retain the core structure of original regimes. Key advantages include better interpretability than non-specialized end-to-end learning, enhanced generalizability across varying network configurations, and faster inference speed compared to optimization-based methods and simulators. The methodologies presented herein have substantial potential for diverse wireless applications, including network design, resource allocation, and network management
The Rise of the Magician State: Remote Viewing, Telepathic Influence, and the Secret History of the Mind-Machine Interface
No full textIn the early 1970s, a small research team funded by the Central Intelligence Agency demonstrated that human consciousness is able to observe and interact with physical objects, organic systems, and sophisticated digital technologies completely hidden – either by distance or shielding – from the perception of any of the physical senses. Since that time, and into the present-day, multiple military departments and federal agencies in Russia, the United States, and China have utilized these abilities to conduct military, law enforcement, intelligence, and other national security operations against their adversaries.
Through a combinatory approach that merged the rich history of magic and mysticism from a variety of cultures with the cutting-edge of science and technology, scientists and operational personnel have sought new ways to more effectively and precisely explore, develop, and deploy these abilities. In this dissertation, I examine how the personnel associated with the programs that emerged conceptualized their own interactions with the technologies used to detect and amplify these abilities, and the various theoretical and spiritual frameworks that they have found useful in conceptualizing these professional and spiritual pursuits.
Through an analysis of the now publicly available information generated by these programs, this dissertation argues that the human mind and the digital world have coevolved over the last fifty years to engage across multiple spheres of conscious, unconscious, and psi interactions continuously all of the time. Thus, this secret history re-examines ancient questions about the nature of consciousness, what it means to be human, and the invisible aspects of reality from the perspective of our secular, hyper-technological, democratic society